Characterization and Activity of ZrO<sub>2</sub> Doped SBA-15 Supported NiMo Catalysts for HDS and HDN of Bitumen Derived Heavy Gas Oil
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Bibliographic record
Abstract
Gas-oil obtained from bitumen contains a significant amount of impurities, which are difficult to remove using a conventional alumina supported hydrotreating catalyst. Innumerable studies have been carried out to develop a highly effective hydrotreating catalyst, and among all utilizing more advanced support is considered as a better alternative. Recently, SBA-15 has received importance as a catalyst support because of its excellent textural properties. However, SBA-15 lacks surface acidity and provides very low metal–support interaction. By modifying SBA-15 with zirconia, an optimum level of surface acidity and Si–Mo interaction can be achieved. Also, by doping zirconia with SBA-15, the textural properties of zirconia can be improved. Hence, a synergistic effect can be obtained while incorporating zirconia onto SBA-15, and the resulting material Zr-SBA-15 can be used as an effective support for a hydrotreating catalyst. Zr-SBA-15 supports were prepared by the post synthesis and the direct synthesis method. Zr-SBA-15 supported NiMo catalysts were prepared by an incipient wetness impregnation technique. Catalysts and supports were characterized by SAXS, BET, XRD, TEM, SEM, ICP-MS, FTIR, and Raman spectroscopy methods. Characterization of support confirmed that the zirconia was successfully incorporated in a mesoporous SBA-15 structure without significantly changing the textural properties of SBA-15. The performance of the Zr-SBA-15 supported NiMo catalysts was evaluated based on hydrodesulfurization and hydrodenitrogenation activities exhibited during hydrotreating of heavy gas oil derived from Athabasca bitumen. The comparison of catalytic activities shows that the NiMo catalysts supported on Zr-SBA-15 exhibited a higher hydrotreating activity compared to γ-Al 2 O 3 and SBA-15 supported catalysts.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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